Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 MiB
Average record size in memory200.0 B

Variable types

Text3
DateTime4
Numeric6
Categorical11
Boolean1

Alerts

loan_amount_allowed is highly overall correlated with sum_assured_inr/coverage_amountHigh correlation
payment_frequency is highly overall correlated with payment_frequency_daysHigh correlation
payment_frequency_days is highly overall correlated with payment_frequencyHigh correlation
policy_code is highly overall correlated with policy_type_code and 1 other fieldsHigh correlation
policy_type_code is highly overall correlated with policy_code and 1 other fieldsHigh correlation
purchase_month is highly overall correlated with purchase_quarterHigh correlation
purchase_quarter is highly overall correlated with purchase_monthHigh correlation
rm_id is highly overall correlated with sales_agent_code and 2 other fieldsHigh correlation
sales_agent_code is highly overall correlated with rm_id and 2 other fieldsHigh correlation
state is highly overall correlated with rm_id and 2 other fieldsHigh correlation
sum_assured_inr/coverage_amount is highly overall correlated with loan_amount_allowedHigh correlation
tenure_(years) is highly overall correlated with policy_code and 1 other fieldsHigh correlation
zonal_manager_id is highly overall correlated with rm_id and 2 other fieldsHigh correlation
policy_status is highly imbalanced (53.5%)Imbalance
sum_assured_inr/coverage_amount has unique valuesUnique

Reproduction

Analysis started2025-11-11 14:19:42.341636
Analysis finished2025-11-11 14:19:58.878970
Duration16.54 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Distinct9938
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2025-11-11T19:49:59.334052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters140000
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9876 ?
Unique (%)98.8%

Sample

1st rowTRS-POL-287638
2nd rowTRS-POL-983850
3rd rowTRS-POL-892387
4th rowTRS-POL-204292
5th rowTRS-POL-621019
ValueCountFrequency (%)
trs-pol-2664002
 
< 0.1%
trs-pol-1146162
 
< 0.1%
trs-pol-7621172
 
< 0.1%
trs-pol-8337192
 
< 0.1%
trs-pol-3146572
 
< 0.1%
trs-pol-3478522
 
< 0.1%
trs-pol-2992622
 
< 0.1%
trs-pol-2488742
 
< 0.1%
trs-pol-1473042
 
< 0.1%
trs-pol-1922722
 
< 0.1%
Other values (9928)9980
99.8%
2025-11-11T19:49:59.916715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
-20000
14.3%
T10000
 
7.1%
R10000
 
7.1%
S10000
 
7.1%
P10000
 
7.1%
O10000
 
7.1%
L10000
 
7.1%
66234
 
4.5%
26171
 
4.4%
76131
 
4.4%
Other values (7)41464
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)140000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
-20000
14.3%
T10000
 
7.1%
R10000
 
7.1%
S10000
 
7.1%
P10000
 
7.1%
O10000
 
7.1%
L10000
 
7.1%
66234
 
4.5%
26171
 
4.4%
76131
 
4.4%
Other values (7)41464
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)140000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
-20000
14.3%
T10000
 
7.1%
R10000
 
7.1%
S10000
 
7.1%
P10000
 
7.1%
O10000
 
7.1%
L10000
 
7.1%
66234
 
4.5%
26171
 
4.4%
76131
 
4.4%
Other values (7)41464
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)140000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
-20000
14.3%
T10000
 
7.1%
R10000
 
7.1%
S10000
 
7.1%
P10000
 
7.1%
O10000
 
7.1%
L10000
 
7.1%
66234
 
4.5%
26171
 
4.4%
76131
 
4.4%
Other values (7)41464
29.6%
Distinct3237
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Minimum2015-01-08 00:00:00
Maximum2024-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-11T19:50:00.090215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:50:00.262123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1841
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Minimum2016-03-28 00:00:00
Maximum2025-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-11T19:50:00.424399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:50:00.585803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

tenure_(years)
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.1789
Minimum10
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-11T19:50:00.724556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q112
median20
Q320
95-th percentile25
Maximum25
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.4460867
Coefficient of variation (CV)0.29958285
Kurtosis-1.2798103
Mean18.1789
Median Absolute Deviation (MAD)5
Skewness-0.32459948
Sum181789
Variance29.659861
MonotonicityNot monotonic
2025-11-11T19:50:00.845323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
204177
41.8%
252358
23.6%
101818
18.2%
121235
 
12.3%
15395
 
4.0%
2217
 
0.2%
ValueCountFrequency (%)
101818
18.2%
121235
 
12.3%
15395
 
4.0%
204177
41.8%
2217
 
0.2%
252358
23.6%
ValueCountFrequency (%)
252358
23.6%
2217
 
0.2%
204177
41.8%
15395
 
4.0%
121235
 
12.3%
101818
18.2%
Distinct3430
Distinct (%)34.3%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Minimum2015-07-24 00:00:00
Maximum2025-07-23 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-11T19:50:00.981904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:50:01.133143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct9945
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2025-11-11T19:50:01.368573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters110000
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9890 ?
Unique (%)98.9%

Sample

1st rowCUST-912138
2nd rowCUST-676760
3rd rowCUST-189349
4th rowCUST-567423
5th rowCUST-292627
ValueCountFrequency (%)
cust-5521092
 
< 0.1%
cust-2909452
 
< 0.1%
cust-3154732
 
< 0.1%
cust-9707132
 
< 0.1%
cust-2836902
 
< 0.1%
cust-3989732
 
< 0.1%
cust-7881062
 
< 0.1%
cust-1416562
 
< 0.1%
cust-2932352
 
< 0.1%
cust-7104772
 
< 0.1%
Other values (9935)9980
99.8%
2025-11-11T19:50:01.665007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C10000
 
9.1%
U10000
 
9.1%
S10000
 
9.1%
T10000
 
9.1%
-10000
 
9.1%
46185
 
5.6%
26170
 
5.6%
56168
 
5.6%
96168
 
5.6%
66090
 
5.5%
Other values (5)29219
26.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)110000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C10000
 
9.1%
U10000
 
9.1%
S10000
 
9.1%
T10000
 
9.1%
-10000
 
9.1%
46185
 
5.6%
26170
 
5.6%
56168
 
5.6%
96168
 
5.6%
66090
 
5.5%
Other values (5)29219
26.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)110000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C10000
 
9.1%
U10000
 
9.1%
S10000
 
9.1%
T10000
 
9.1%
-10000
 
9.1%
46185
 
5.6%
26170
 
5.6%
56168
 
5.6%
96168
 
5.6%
66090
 
5.5%
Other values (5)29219
26.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)110000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C10000
 
9.1%
U10000
 
9.1%
S10000
 
9.1%
T10000
 
9.1%
-10000
 
9.1%
46185
 
5.6%
26170
 
5.6%
56168
 
5.6%
96168
 
5.6%
66090
 
5.5%
Other values (5)29219
26.6%

sum_assured_inr/coverage_amount
Real number (ℝ)

High correlation  Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1054222.3
Minimum100316.82
Maximum1999546.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-11T19:50:01.778462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum100316.82
5-th percentile196491.26
Q1577241.91
median1059397.7
Q31527198.5
95-th percentile1907294.4
Maximum1999546.8
Range1899229.9
Interquartile range (IQR)949956.59

Descriptive statistics

Standard deviation549260.89
Coefficient of variation (CV)0.52101051
Kurtosis-1.2055452
Mean1054222.3
Median Absolute Deviation (MAD)475548.59
Skewness-0.0035187733
Sum1.0542223 × 1010
Variance3.0168752 × 1011
MonotonicityNot monotonic
2025-11-11T19:50:01.941012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
684200.641
 
< 0.1%
1006981.051
 
< 0.1%
391180.061
 
< 0.1%
1153296.541
 
< 0.1%
1176121.671
 
< 0.1%
1572200.771
 
< 0.1%
1735695.761
 
< 0.1%
1185416.851
 
< 0.1%
995666.541
 
< 0.1%
662242.361
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
100316.821
< 0.1%
100333.641
< 0.1%
100632.551
< 0.1%
100665.031
< 0.1%
100709.941
< 0.1%
100828.111
< 0.1%
101628.271
< 0.1%
101648.771
< 0.1%
102350.251
< 0.1%
102430.361
< 0.1%
ValueCountFrequency (%)
1999546.761
< 0.1%
1999441.681
< 0.1%
1999264.171
< 0.1%
1998796.461
< 0.1%
1998525.181
< 0.1%
1998314.161
< 0.1%
1998311.261
< 0.1%
1998270.051
< 0.1%
1997605.611
< 0.1%
1997550.351
< 0.1%

premium_amount
Real number (ℝ)

Distinct9995
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52261.483
Minimum5028.98
Maximum99997.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-11T19:50:02.118779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5028.98
5-th percentile9641.422
Q128251.645
median52362.055
Q376051.84
95-th percentile95333.279
Maximum99997.01
Range94968.03
Interquartile range (IQR)47800.195

Descriptive statistics

Standard deviation27511.888
Coefficient of variation (CV)0.52642762
Kurtosis-1.2063144
Mean52261.483
Median Absolute Deviation (MAD)23881.73
Skewness0.011607776
Sum5.2261483 × 108
Variance7.5690399 × 108
MonotonicityNot monotonic
2025-11-11T19:50:02.292853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63835.332
 
< 0.1%
58252.592
 
< 0.1%
41203.822
 
< 0.1%
52662.242
 
< 0.1%
20252.272
 
< 0.1%
25882.431
 
< 0.1%
62045.931
 
< 0.1%
5973.761
 
< 0.1%
33635.081
 
< 0.1%
47823.591
 
< 0.1%
Other values (9985)9985
99.9%
ValueCountFrequency (%)
5028.981
< 0.1%
5055.751
< 0.1%
5060.841
< 0.1%
5066.791
< 0.1%
5082.211
< 0.1%
5089.121
< 0.1%
5093.811
< 0.1%
5096.331
< 0.1%
5096.91
< 0.1%
5109.711
< 0.1%
ValueCountFrequency (%)
99997.011
< 0.1%
99976.711
< 0.1%
99974.541
< 0.1%
99965.591
< 0.1%
99958.741
< 0.1%
99944.011
< 0.1%
99938.141
< 0.1%
99932.811
< 0.1%
99925.511
< 0.1%
99920.711
< 0.1%

payment_frequency
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Quarterly
3432 
Monthly
3324 
Annually
3244 

Length

Max length9
Median length8
Mean length8.0108
Min length7

Characters and Unicode

Total characters80108
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMonthly
2nd rowAnnually
3rd rowQuarterly
4th rowAnnually
5th rowMonthly

Common Values

ValueCountFrequency (%)
Quarterly3432
34.3%
Monthly3324
33.2%
Annually3244
32.4%

Length

2025-11-11T19:50:02.440418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T19:50:02.541328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
quarterly3432
34.3%
monthly3324
33.2%
annually3244
32.4%

Most occurring characters

ValueCountFrequency (%)
l13244
16.5%
y10000
12.5%
n9812
12.2%
r6864
8.6%
t6756
8.4%
a6676
8.3%
u6676
8.3%
Q3432
 
4.3%
e3432
 
4.3%
M3324
 
4.1%
Other values (3)9892
12.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)80108
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l13244
16.5%
y10000
12.5%
n9812
12.2%
r6864
8.6%
t6756
8.4%
a6676
8.3%
u6676
8.3%
Q3432
 
4.3%
e3432
 
4.3%
M3324
 
4.1%
Other values (3)9892
12.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)80108
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l13244
16.5%
y10000
12.5%
n9812
12.2%
r6864
8.6%
t6756
8.4%
a6676
8.3%
u6676
8.3%
Q3432
 
4.3%
e3432
 
4.3%
M3324
 
4.1%
Other values (3)9892
12.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)80108
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l13244
16.5%
y10000
12.5%
n9812
12.2%
r6864
8.6%
t6756
8.4%
a6676
8.3%
u6676
8.3%
Q3432
 
4.3%
e3432
 
4.3%
M3324
 
4.1%
Other values (3)9892
12.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
True
5108 
False
4892 
ValueCountFrequency (%)
True5108
51.1%
False4892
48.9%
2025-11-11T19:50:02.606755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

loan_amount_allowed
Real number (ℝ)

High correlation 

Distinct9999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean475327.13
Minimum30894.54
Maximum1181952.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-11T19:50:02.705455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum30894.54
5-th percentile85841.391
Q1248153.4
median457602.98
Q3672625.19
95-th percentile950570.58
Maximum1181952.2
Range1151057.6
Interquartile range (IQR)424471.79

Descriptive statistics

Standard deviation267747.04
Coefficient of variation (CV)0.56329005
Kurtosis-0.77715878
Mean475327.13
Median Absolute Deviation (MAD)211792.03
Skewness0.30950321
Sum4.7532713 × 109
Variance7.1688479 × 1010
MonotonicityNot monotonic
2025-11-11T19:50:02.871543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
687043.282
 
< 0.1%
447163.561
 
< 0.1%
251903.51
 
< 0.1%
513299.271
 
< 0.1%
199802.211
 
< 0.1%
709501.061
 
< 0.1%
73432.271
 
< 0.1%
361902.111
 
< 0.1%
732352.541
 
< 0.1%
864927.021
 
< 0.1%
Other values (9989)9989
99.9%
ValueCountFrequency (%)
30894.541
< 0.1%
32478.831
< 0.1%
32687.281
< 0.1%
32738.111
< 0.1%
33423.821
< 0.1%
34203.181
< 0.1%
34449.911
< 0.1%
34992.621
< 0.1%
35106.171
< 0.1%
35398.121
< 0.1%
ValueCountFrequency (%)
1181952.171
< 0.1%
1180406.451
< 0.1%
1180065.661
< 0.1%
1173589.51
< 0.1%
1168924.571
< 0.1%
1166546.61
< 0.1%
1163269.881
< 0.1%
1161643.031
< 0.1%
1161325.841
< 0.1%
1161317.061
< 0.1%

underwriting_expenses
Real number (ℝ)

Distinct9938
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5270.0628
Minimum500.6
Maximum9999.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-11T19:50:03.066528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum500.6
5-th percentile971.745
Q12853.765
median5264.165
Q37702.4975
95-th percentile9542.81
Maximum9999.78
Range9499.18
Interquartile range (IQR)4848.7325

Descriptive statistics

Standard deviation2774.4241
Coefficient of variation (CV)0.52644991
Kurtosis-1.2283104
Mean5270.0628
Median Absolute Deviation (MAD)2422.77
Skewness-0.0086640626
Sum52700628
Variance7697429.1
MonotonicityNot monotonic
2025-11-11T19:50:03.211790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8312.832
 
< 0.1%
1976.242
 
< 0.1%
2890.142
 
< 0.1%
2861.032
 
< 0.1%
8542.352
 
< 0.1%
9036.622
 
< 0.1%
4537.452
 
< 0.1%
1290.932
 
< 0.1%
6207.642
 
< 0.1%
5208.182
 
< 0.1%
Other values (9928)9980
99.8%
ValueCountFrequency (%)
500.61
< 0.1%
500.71
< 0.1%
501.091
< 0.1%
501.271
< 0.1%
502.191
< 0.1%
502.211
< 0.1%
502.291
< 0.1%
502.371
< 0.1%
503.831
< 0.1%
504.061
< 0.1%
ValueCountFrequency (%)
9999.781
< 0.1%
9998.661
< 0.1%
9998.651
< 0.1%
9998.471
< 0.1%
9998.311
< 0.1%
9997.821
< 0.1%
9997.471
< 0.1%
9996.081
< 0.1%
9994.771
< 0.1%
9994.621
< 0.1%

sales_agent_code
Categorical

High correlation 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
AGT-6578
1221 
AGT-3567
1120 
AGT-3488
1009 
AGT-3150
819 
AGT-2345
637 
Other values (17)
5194 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters80000
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAGT-6578
2nd rowAGT-6578
3rd rowAGT-6578
4th rowAGT-6578
5th rowAGT-6578

Common Values

ValueCountFrequency (%)
AGT-65781221
12.2%
AGT-35671120
11.2%
AGT-34881009
 
10.1%
AGT-3150819
 
8.2%
AGT-2345637
 
6.4%
AGT-5980612
 
6.1%
AGT-3465550
 
5.5%
AGT-4456512
 
5.1%
AGT-5467463
 
4.6%
AGT-7898458
 
4.6%
Other values (12)2599
26.0%

Length

2025-11-11T19:50:03.339568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
agt-65781221
12.2%
agt-35671120
11.2%
agt-34881009
 
10.1%
agt-3150819
 
8.2%
agt-2345637
 
6.4%
agt-5980612
 
6.1%
agt-3465550
 
5.5%
agt-4456512
 
5.1%
agt-5467463
 
4.6%
agt-7898458
 
4.6%
Other values (12)2599
26.0%

Most occurring characters

ValueCountFrequency (%)
A10000
12.5%
G10000
12.5%
T10000
12.5%
-10000
12.5%
57322
9.2%
45784
7.2%
85703
7.1%
34385
5.5%
63967
 
5.0%
93687
 
4.6%
Other values (4)9152
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)80000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A10000
12.5%
G10000
12.5%
T10000
12.5%
-10000
12.5%
57322
9.2%
45784
7.2%
85703
7.1%
34385
5.5%
63967
 
5.0%
93687
 
4.6%
Other values (4)9152
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)80000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A10000
12.5%
G10000
12.5%
T10000
12.5%
-10000
12.5%
57322
9.2%
45784
7.2%
85703
7.1%
34385
5.5%
63967
 
5.0%
93687
 
4.6%
Other values (4)9152
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)80000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A10000
12.5%
G10000
12.5%
T10000
12.5%
-10000
12.5%
57322
9.2%
45784
7.2%
85703
7.1%
34385
5.5%
63967
 
5.0%
93687
 
4.6%
Other values (4)9152
11.4%

purchase_month
Categorical

High correlation 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
December
892 
March
874 
August
866 
January
855 
July
841 
Other values (7)
5672 

Length

Max length9
Median length7
Mean length6.165
Min length3

Characters and Unicode

Total characters61650
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJanuary
2nd rowOctober
3rd rowApril
4th rowNovember
5th rowFebruary

Common Values

ValueCountFrequency (%)
December892
8.9%
March874
8.7%
August866
8.7%
January855
8.6%
July841
8.4%
November840
8.4%
October828
8.3%
May827
8.3%
April825
8.2%
September817
8.2%
Other values (2)1535
15.3%

Length

2025-11-11T19:50:03.456306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
december892
8.9%
march874
8.7%
august866
8.7%
january855
8.6%
july841
8.4%
november840
8.4%
october828
8.3%
may827
8.3%
april825
8.2%
september817
8.2%
Other values (2)1535
15.3%

Most occurring characters

ValueCountFrequency (%)
e9170
14.9%
r7423
12.0%
u4963
 
8.1%
a4157
 
6.7%
b4123
 
6.7%
y3269
 
5.3%
c2594
 
4.2%
m2549
 
4.1%
t2511
 
4.1%
J2485
 
4.0%
Other values (16)18406
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)61650
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e9170
14.9%
r7423
12.0%
u4963
 
8.1%
a4157
 
6.7%
b4123
 
6.7%
y3269
 
5.3%
c2594
 
4.2%
m2549
 
4.1%
t2511
 
4.1%
J2485
 
4.0%
Other values (16)18406
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)61650
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e9170
14.9%
r7423
12.0%
u4963
 
8.1%
a4157
 
6.7%
b4123
 
6.7%
y3269
 
5.3%
c2594
 
4.2%
m2549
 
4.1%
t2511
 
4.1%
J2485
 
4.0%
Other values (16)18406
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)61650
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e9170
14.9%
r7423
12.0%
u4963
 
8.1%
a4157
 
6.7%
b4123
 
6.7%
y3269
 
5.3%
c2594
 
4.2%
m2549
 
4.1%
t2511
 
4.1%
J2485
 
4.0%
Other values (16)18406
29.9%

purchase_quarter
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Q4
2560 
Q3
2524 
Q1
2475 
Q2
2441 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters20000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQ1
2nd rowQ4
3rd rowQ2
4th rowQ4
5th rowQ1

Common Values

ValueCountFrequency (%)
Q42560
25.6%
Q32524
25.2%
Q12475
24.8%
Q22441
24.4%

Length

2025-11-11T19:50:03.561638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T19:50:03.650931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
q42560
25.6%
q32524
25.2%
q12475
24.8%
q22441
24.4%

Most occurring characters

ValueCountFrequency (%)
Q10000
50.0%
42560
 
12.8%
32524
 
12.6%
12475
 
12.4%
22441
 
12.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)20000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Q10000
50.0%
42560
 
12.8%
32524
 
12.6%
12475
 
12.4%
22441
 
12.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)20000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Q10000
50.0%
42560
 
12.8%
32524
 
12.6%
12475
 
12.4%
22441
 
12.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)20000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Q10000
50.0%
42560
 
12.8%
32524
 
12.6%
12475
 
12.4%
22441
 
12.2%

purchase_year
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.0509
Minimum2015
Maximum2025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-11T19:50:03.739915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2016
Q12018
median2020
Q32023
95-th percentile2025
Maximum2025
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.9134787
Coefficient of variation (CV)0.0014422798
Kurtosis-1.1553952
Mean2020.0509
Median Absolute Deviation (MAD)2
Skewness0.004066033
Sum20200509
Variance8.488358
MonotonicityNot monotonic
2025-11-11T19:50:03.843454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
20181092
10.9%
20221021
10.2%
20241019
10.2%
2016994
9.9%
2019990
9.9%
2021983
9.8%
2020977
9.8%
2023974
9.7%
2017953
9.5%
2025548
5.5%
ValueCountFrequency (%)
2015449
4.5%
2016994
9.9%
2017953
9.5%
20181092
10.9%
2019990
9.9%
2020977
9.8%
2021983
9.8%
20221021
10.2%
2023974
9.7%
20241019
10.2%
ValueCountFrequency (%)
2025548
5.5%
20241019
10.2%
2023974
9.7%
20221021
10.2%
2021983
9.8%
2020977
9.8%
2019990
9.9%
20181092
10.9%
2017953
9.5%
2016994
9.9%
Distinct3428
Distinct (%)34.3%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Minimum2016-07-24 00:00:00
Maximum2026-07-23 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-11T19:50:03.994981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:50:04.155410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct9939
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2025-11-11T19:50:04.407730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters100000
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9879 ?
Unique (%)98.8%

Sample

1st rowCLM-163983
2nd rowCLM-729665
3rd rowCLM-655001
4th rowCLM-101156
5th rowCLM-762598
ValueCountFrequency (%)
clm-5728153
 
< 0.1%
clm-8430332
 
< 0.1%
clm-3962452
 
< 0.1%
clm-2586972
 
< 0.1%
clm-1097532
 
< 0.1%
clm-6875622
 
< 0.1%
clm-4531102
 
< 0.1%
clm-7493782
 
< 0.1%
clm-6200622
 
< 0.1%
clm-8333082
 
< 0.1%
Other values (9929)9979
99.8%
2025-11-11T19:50:04.715138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C10000
10.0%
L10000
10.0%
M10000
10.0%
-10000
10.0%
56217
 
6.2%
76192
 
6.2%
66140
 
6.1%
36129
 
6.1%
16114
 
6.1%
26097
 
6.1%
Other values (4)23111
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C10000
10.0%
L10000
10.0%
M10000
10.0%
-10000
10.0%
56217
 
6.2%
76192
 
6.2%
66140
 
6.1%
36129
 
6.1%
16114
 
6.1%
26097
 
6.1%
Other values (4)23111
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C10000
10.0%
L10000
10.0%
M10000
10.0%
-10000
10.0%
56217
 
6.2%
76192
 
6.2%
66140
 
6.1%
36129
 
6.1%
16114
 
6.1%
26097
 
6.1%
Other values (4)23111
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C10000
10.0%
L10000
10.0%
M10000
10.0%
-10000
10.0%
56217
 
6.2%
76192
 
6.2%
66140
 
6.1%
36129
 
6.1%
16114
 
6.1%
26097
 
6.1%
Other values (4)23111
23.1%

policy_type_code
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
TR-ENDD989
3383 
TR-WHCD968
3347 
TR-UNCD988
3270 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters100000
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTR-UNCD988
2nd rowTR-UNCD988
3rd rowTR-UNCD988
4th rowTR-UNCD988
5th rowTR-UNCD988

Common Values

ValueCountFrequency (%)
TR-ENDD9893383
33.8%
TR-WHCD9683347
33.5%
TR-UNCD9883270
32.7%

Length

2025-11-11T19:50:04.820282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T19:50:04.898055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
tr-endd9893383
33.8%
tr-whcd9683347
33.5%
tr-uncd9883270
32.7%

Most occurring characters

ValueCountFrequency (%)
D13383
13.4%
913383
13.4%
813270
13.3%
R10000
10.0%
T10000
10.0%
-10000
10.0%
N6653
6.7%
C6617
6.6%
E3383
 
3.4%
W3347
 
3.3%
Other values (3)9964
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D13383
13.4%
913383
13.4%
813270
13.3%
R10000
10.0%
T10000
10.0%
-10000
10.0%
N6653
6.7%
C6617
6.6%
E3383
 
3.4%
W3347
 
3.3%
Other values (3)9964
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D13383
13.4%
913383
13.4%
813270
13.3%
R10000
10.0%
T10000
10.0%
-10000
10.0%
N6653
6.7%
C6617
6.6%
E3383
 
3.4%
W3347
 
3.3%
Other values (3)9964
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D13383
13.4%
913383
13.4%
813270
13.3%
R10000
10.0%
T10000
10.0%
-10000
10.0%
N6653
6.7%
C6617
6.6%
E3383
 
3.4%
W3347
 
3.3%
Other values (3)9964
10.0%

policy_code
Categorical

High correlation 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
UGP-TRCD-9834
3205 
JSU-TRCD-9813
2358 
LGA-TRCD-9829
1818 
STP-TRCD-9812
1236 
ICP-TRCD-9832
972 
Other values (3)
411 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters130000
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUGP-TRCD-9834
2nd rowUGP-TRCD-9834
3rd rowUGP-TRCD-9834
4th rowUGP-TRCD-9834
5th rowUGP-TRCD-9834

Common Values

ValueCountFrequency (%)
UGP-TRCD-98343205
32.0%
JSU-TRCD-98132358
23.6%
LGA-TRCD-98291818
18.2%
STP-TRCD-98121236
 
12.4%
ICP-TRCD-9832972
 
9.7%
WLS-TRCD-9814329
 
3.3%
RSE-TRCD-985665
 
0.7%
GWB-TRCD-983317
 
0.2%

Length

2025-11-11T19:50:05.001905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T19:50:05.102864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ugp-trcd-98343205
32.0%
jsu-trcd-98132358
23.6%
lga-trcd-98291818
18.2%
stp-trcd-98121236
 
12.4%
icp-trcd-9832972
 
9.7%
wls-trcd-9814329
 
3.3%
rse-trcd-985665
 
0.7%
gwb-trcd-983317
 
0.2%

Most occurring characters

ValueCountFrequency (%)
-20000
15.4%
911818
9.1%
T11236
 
8.6%
C10972
 
8.4%
R10065
 
7.7%
D10000
 
7.7%
810000
 
7.7%
36569
 
5.1%
U5563
 
4.3%
P5413
 
4.2%
Other values (14)28364
21.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)130000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
-20000
15.4%
911818
9.1%
T11236
 
8.6%
C10972
 
8.4%
R10065
 
7.7%
D10000
 
7.7%
810000
 
7.7%
36569
 
5.1%
U5563
 
4.3%
P5413
 
4.2%
Other values (14)28364
21.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)130000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
-20000
15.4%
911818
9.1%
T11236
 
8.6%
C10972
 
8.4%
R10065
 
7.7%
D10000
 
7.7%
810000
 
7.7%
36569
 
5.1%
U5563
 
4.3%
P5413
 
4.2%
Other values (14)28364
21.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)130000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
-20000
15.4%
911818
9.1%
T11236
 
8.6%
C10972
 
8.4%
R10065
 
7.7%
D10000
 
7.7%
810000
 
7.7%
36569
 
5.1%
U5563
 
4.3%
P5413
 
4.2%
Other values (14)28364
21.8%

policy_status
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Active
7332 
Surrendered
2524 
Lapsed
 
107
Claimed
 
37

Length

Max length11
Median length6
Mean length7.2657
Min length6

Characters and Unicode

Total characters72657
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowActive
2nd rowActive
3rd rowActive
4th rowActive
5th rowActive

Common Values

ValueCountFrequency (%)
Active7332
73.3%
Surrendered2524
 
25.2%
Lapsed107
 
1.1%
Claimed37
 
0.4%

Length

2025-11-11T19:50:05.259924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T19:50:05.351754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
active7332
73.3%
surrendered2524
 
25.2%
lapsed107
 
1.1%
claimed37
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e15048
20.7%
r7572
10.4%
i7369
10.1%
A7332
10.1%
c7332
10.1%
t7332
10.1%
v7332
10.1%
d5192
 
7.1%
u2524
 
3.5%
S2524
 
3.5%
Other values (8)3100
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)72657
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e15048
20.7%
r7572
10.4%
i7369
10.1%
A7332
10.1%
c7332
10.1%
t7332
10.1%
v7332
10.1%
d5192
 
7.1%
u2524
 
3.5%
S2524
 
3.5%
Other values (8)3100
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)72657
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e15048
20.7%
r7572
10.4%
i7369
10.1%
A7332
10.1%
c7332
10.1%
t7332
10.1%
v7332
10.1%
d5192
 
7.1%
u2524
 
3.5%
S2524
 
3.5%
Other values (8)3100
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)72657
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e15048
20.7%
r7572
10.4%
i7369
10.1%
A7332
10.1%
c7332
10.1%
t7332
10.1%
v7332
10.1%
d5192
 
7.1%
u2524
 
3.5%
S2524
 
3.5%
Other values (8)3100
 
4.3%

state
Categorical

High correlation 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Delhi
1221 
Uttar Pradesh
1120 
Sikkim
1009 
Haryana
819 
Madhya Pradesh
637 
Other values (17)
5194 

Length

Max length16
Median length13
Mean length8.8023
Min length3

Characters and Unicode

Total characters88023
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDelhi
2nd rowDelhi
3rd rowDelhi
4th rowDelhi
5th rowDelhi

Common Values

ValueCountFrequency (%)
Delhi1221
12.2%
Uttar Pradesh1120
11.2%
Sikkim1009
 
10.1%
Haryana819
 
8.2%
Madhya Pradesh637
 
6.4%
Tamilnadu612
 
6.1%
Rajasthan550
 
5.5%
Bihar512
 
5.1%
Chandigarh463
 
4.6%
Uttarakhand458
 
4.6%
Other values (12)2599
26.0%

Length

2025-11-11T19:50:05.452416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pradesh2301
18.5%
delhi1221
 
9.8%
uttar1120
 
9.0%
sikkim1009
 
8.1%
haryana819
 
6.6%
madhya637
 
5.1%
tamilnadu612
 
4.9%
rajasthan550
 
4.4%
bihar512
 
4.1%
chandigarh463
 
3.7%
Other values (14)3209
25.8%

Most occurring characters

ValueCountFrequency (%)
a17899
20.3%
h8562
 
9.7%
r7840
 
8.9%
d5240
 
6.0%
i5236
 
5.9%
e4402
 
5.0%
t4319
 
4.9%
n4169
 
4.7%
s3949
 
4.5%
k2992
 
3.4%
Other values (25)23415
26.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)88023
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a17899
20.3%
h8562
 
9.7%
r7840
 
8.9%
d5240
 
6.0%
i5236
 
5.9%
e4402
 
5.0%
t4319
 
4.9%
n4169
 
4.7%
s3949
 
4.5%
k2992
 
3.4%
Other values (25)23415
26.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)88023
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a17899
20.3%
h8562
 
9.7%
r7840
 
8.9%
d5240
 
6.0%
i5236
 
5.9%
e4402
 
5.0%
t4319
 
4.9%
n4169
 
4.7%
s3949
 
4.5%
k2992
 
3.4%
Other values (25)23415
26.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)88023
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a17899
20.3%
h8562
 
9.7%
r7840
 
8.9%
d5240
 
6.0%
i5236
 
5.9%
e4402
 
5.0%
t4319
 
4.9%
n4169
 
4.7%
s3949
 
4.5%
k2992
 
3.4%
Other values (25)23415
26.6%

rm_id
Categorical

High correlation 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
RMN7A3X1
3799 
RMC9F7L5
2018 
RMS4B9K2
1661 
RME2D5Q4
1509 
RMW8C6P3
1013 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters80000
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRMN7A3X1
2nd rowRMN7A3X1
3rd rowRMN7A3X1
4th rowRMN7A3X1
5th rowRMN7A3X1

Common Values

ValueCountFrequency (%)
RMN7A3X13799
38.0%
RMC9F7L52018
20.2%
RMS4B9K21661
16.6%
RME2D5Q41509
 
15.1%
RMW8C6P31013
 
10.1%

Length

2025-11-11T19:50:05.555641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T19:50:05.643670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
rmn7a3x13799
38.0%
rmc9f7l52018
20.2%
rms4b9k21661
16.6%
rme2d5q41509
 
15.1%
rmw8c6p31013
 
10.1%

Most occurring characters

ValueCountFrequency (%)
R10000
 
12.5%
M10000
 
12.5%
75817
 
7.3%
34812
 
6.0%
N3799
 
4.7%
A3799
 
4.7%
X3799
 
4.7%
13799
 
4.7%
93679
 
4.6%
53527
 
4.4%
Other values (15)26969
33.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)80000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R10000
 
12.5%
M10000
 
12.5%
75817
 
7.3%
34812
 
6.0%
N3799
 
4.7%
A3799
 
4.7%
X3799
 
4.7%
13799
 
4.7%
93679
 
4.6%
53527
 
4.4%
Other values (15)26969
33.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)80000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R10000
 
12.5%
M10000
 
12.5%
75817
 
7.3%
34812
 
6.0%
N3799
 
4.7%
A3799
 
4.7%
X3799
 
4.7%
13799
 
4.7%
93679
 
4.6%
53527
 
4.4%
Other values (15)26969
33.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)80000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R10000
 
12.5%
M10000
 
12.5%
75817
 
7.3%
34812
 
6.0%
N3799
 
4.7%
A3799
 
4.7%
X3799
 
4.7%
13799
 
4.7%
93679
 
4.6%
53527
 
4.4%
Other values (15)26969
33.7%

zonal_manager_id
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
ZMN8K2L1
5817 
ZMW3T7P8
2522 
ZMS5Q4R2
1661 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters80000
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowZMN8K2L1
2nd rowZMN8K2L1
3rd rowZMN8K2L1
4th rowZMN8K2L1
5th rowZMN8K2L1

Common Values

ValueCountFrequency (%)
ZMN8K2L15817
58.2%
ZMW3T7P82522
25.2%
ZMS5Q4R21661
 
16.6%

Length

2025-11-11T19:50:05.761831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T19:50:05.834417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
zmn8k2l15817
58.2%
zmw3t7p82522
25.2%
zms5q4r21661
 
16.6%

Most occurring characters

ValueCountFrequency (%)
Z10000
12.5%
M10000
12.5%
88339
10.4%
27478
9.3%
N5817
 
7.3%
K5817
 
7.3%
L5817
 
7.3%
15817
 
7.3%
W2522
 
3.2%
32522
 
3.2%
Other values (8)15871
19.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)80000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Z10000
12.5%
M10000
12.5%
88339
10.4%
27478
9.3%
N5817
 
7.3%
K5817
 
7.3%
L5817
 
7.3%
15817
 
7.3%
W2522
 
3.2%
32522
 
3.2%
Other values (8)15871
19.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)80000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Z10000
12.5%
M10000
12.5%
88339
10.4%
27478
9.3%
N5817
 
7.3%
K5817
 
7.3%
L5817
 
7.3%
15817
 
7.3%
W2522
 
3.2%
32522
 
3.2%
Other values (8)15871
19.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)80000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Z10000
12.5%
M10000
12.5%
88339
10.4%
27478
9.3%
N5817
 
7.3%
K5817
 
7.3%
L5817
 
7.3%
15817
 
7.3%
W2522
 
3.2%
32522
 
3.2%
Other values (8)15871
19.8%

payment_frequency_days
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
90
3432 
30
3324 
365
3244 

Length

Max length3
Median length2
Mean length2.3244
Min length2

Characters and Unicode

Total characters23244
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30
2nd row365
3rd row90
4th row365
5th row30

Common Values

ValueCountFrequency (%)
903432
34.3%
303324
33.2%
3653244
32.4%

Length

2025-11-11T19:50:05.942503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T19:50:06.028876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
903432
34.3%
303324
33.2%
3653244
32.4%

Most occurring characters

ValueCountFrequency (%)
06756
29.1%
36568
28.3%
93432
14.8%
63244
14.0%
53244
14.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)23244
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
06756
29.1%
36568
28.3%
93432
14.8%
63244
14.0%
53244
14.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)23244
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
06756
29.1%
36568
28.3%
93432
14.8%
63244
14.0%
53244
14.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)23244
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
06756
29.1%
36568
28.3%
93432
14.8%
63244
14.0%
53244
14.0%

Interactions

2025-11-11T19:49:56.768844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:52.676479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:53.511579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:54.280416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:55.305845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:56.065497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:56.884495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:52.889302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:53.645573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:54.390320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:55.426718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:56.178097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:57.009693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:53.029411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:53.777647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:54.757642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:55.553768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:56.304086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:57.120655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:53.151531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:53.902775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:54.963183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:55.664390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:56.422739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:57.237250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:53.274342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:54.024622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:55.081221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:55.783392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:56.546948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:57.346421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:53.397342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:54.160153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:55.197350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:55.938547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T19:49:56.657861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-11T19:50:06.124850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
loan_amount_allowedloan_eligiblepayment_frequencypayment_frequency_dayspolicy_codepolicy_statuspolicy_type_codepremium_amountpurchase_monthpurchase_quarterpurchase_yearrm_idsales_agent_codestatesum_assured_inr/coverage_amounttenure_(years)underwriting_expenseszonal_manager_id
loan_amount_allowed1.0000.0000.0220.0220.0060.0080.013-0.0060.0100.0110.0020.0000.0000.0000.937-0.007-0.0100.000
loan_eligible0.0001.0000.0000.0000.0210.0290.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0080.000
payment_frequency0.0220.0001.0001.0000.0130.0070.0100.0000.0220.0200.0000.0090.0260.0260.0190.0000.0000.004
payment_frequency_days0.0220.0001.0001.0000.0130.0070.0100.0000.0220.0200.0000.0090.0260.0260.0190.0000.0000.004
policy_code0.0060.0210.0130.0131.0000.1231.0000.0100.0000.0050.0000.1630.2320.2320.0001.0000.0240.217
policy_status0.0080.0290.0070.0070.1231.0000.0000.0050.0000.0000.0000.1380.2980.2980.0100.0860.0070.072
policy_type_code0.0130.0000.0100.0101.0000.0001.0000.0070.0000.0110.0050.0040.0360.0360.0000.8710.0000.004
premium_amount-0.0060.0000.0000.0000.0100.0050.0071.0000.0090.003-0.0150.0150.0060.006-0.007-0.0090.0080.023
purchase_month0.0100.0000.0220.0220.0000.0000.0000.0091.0001.0000.0820.0090.0000.0000.0170.0060.0150.013
purchase_quarter0.0110.0050.0200.0200.0050.0000.0110.0031.0001.0000.1330.0120.0000.0000.0220.0070.0080.006
purchase_year0.0020.0000.0000.0000.0000.0000.005-0.0150.0820.1331.0000.0090.0050.0050.010-0.009-0.0160.000
rm_id0.0000.0000.0090.0090.1630.1380.0040.0150.0090.0120.0091.0000.9990.9990.0000.1080.0001.000
sales_agent_code0.0000.0000.0260.0260.2320.2980.0360.0060.0000.0000.0050.9991.0001.0000.0000.1830.0050.999
state0.0000.0000.0260.0260.2320.2980.0360.0060.0000.0000.0050.9991.0001.0000.0000.1830.0050.999
sum_assured_inr/coverage_amount0.9370.0000.0190.0190.0000.0100.000-0.0070.0170.0220.0100.0000.0000.0001.000-0.007-0.0090.000
tenure_(years)-0.0070.0000.0000.0001.0000.0860.871-0.0090.0060.007-0.0090.1080.1830.183-0.0071.0000.0090.145
underwriting_expenses-0.0100.0080.0000.0000.0240.0070.0000.0080.0150.008-0.0160.0000.0050.005-0.0090.0091.0000.005
zonal_manager_id0.0000.0000.0040.0040.2170.0720.0040.0230.0130.0060.0001.0000.9990.9990.0000.1450.0051.000

Missing values

2025-11-11T19:49:57.567201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-11T19:49:57.859297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

policy_numberstart_datelast_paid_datetenure_(years)date_of_purchasecustomer_idsum_assured_inr/coverage_amountpremium_amountpayment_frequencyloan_eligibleloan_amount_allowedunderwriting_expensessales_agent_codepurchase_monthpurchase_quarterpurchase_yearpolicy_anniversary_dateclaim_idpolicy_type_codepolicy_codepolicy_statusstaterm_idzonal_manager_idpayment_frequency_days
0TRS-POL-28763828-01-201728-Jan-252028-Jan-17CUST-9121381837929.1318946.30MonthlyNo709501.069003.34AGT-6578JanuaryQ1201728-Jan-18CLM-163983TR-UNCD988UGP-TRCD-9834ActiveDelhiRMN7A3X1ZMN8K2L130
1TRS-POL-98385027-10-201727-Oct-252027-Oct-17CUST-676760143416.1664648.36AnnuallyYes73432.272808.23AGT-6578OctoberQ4201727-Oct-18CLM-729665TR-UNCD988UGP-TRCD-9834ActiveDelhiRMN7A3X1ZMN8K2L1365
2TRS-POL-89238718-04-202218-Apr-252018-Apr-22CUST-189349619301.1742085.47QuarterlyYes361902.113335.88AGT-6578AprilQ2202218-Apr-23CLM-655001TR-UNCD988UGP-TRCD-9834ActiveDelhiRMN7A3X1ZMN8K2L190
3TRS-POL-20429220-11-201920-Nov-252020-Nov-19CUST-5674231852545.6297105.30AnnuallyYes732352.544715.71AGT-6578NovemberQ4201920-Nov-20CLM-101156TR-UNCD988UGP-TRCD-9834ActiveDelhiRMN7A3X1ZMN8K2L1365
4TRS-POL-62101913-02-202313-Feb-252013-Feb-23CUST-2926271882565.4289233.91MonthlyNo864927.025166.29AGT-6578FebruaryQ1202313-Feb-24CLM-762598TR-UNCD988UGP-TRCD-9834ActiveDelhiRMN7A3X1ZMN8K2L130
5TRS-POL-60390019-04-201919-Apr-252019-Apr-19CUST-8028121330929.4424594.09QuarterlyYes503997.377587.61AGT-6578AprilQ2201919-Apr-20CLM-739868TR-UNCD988UGP-TRCD-9834ActiveDelhiRMN7A3X1ZMN8K2L190
6TRS-POL-37185026-08-201626-Aug-252026-Aug-16CUST-2851151852113.2633450.86AnnuallyNo824389.817388.20AGT-6578AugustQ3201626-Aug-17CLM-590747TR-UNCD988UGP-TRCD-9834ActiveDelhiRMN7A3X1ZMN8K2L1365
7TRS-POL-33617412-08-201912-Aug-252012-Aug-19CUST-541584714993.6276687.52QuarterlyYes261604.734288.23AGT-6578AugustQ3201912-Aug-20CLM-597333TR-UNCD988UGP-TRCD-9834ActiveDelhiRMN7A3X1ZMN8K2L190
8TRS-POL-74585314-11-201614-Nov-252014-Nov-16CUST-192788270848.2618826.75QuarterlyYes141297.416725.00AGT-6578NovemberQ4201614-Nov-17CLM-765047TR-UNCD988UGP-TRCD-9834ActiveDelhiRMN7A3X1ZMN8K2L190
9TRS-POL-19219106-01-202306-Jan-252006-Jan-23CUST-2267781500910.1271206.95AnnuallyYes886258.091346.17AGT-6578JanuaryQ1202306-Jan-24CLM-790890TR-UNCD988UGP-TRCD-9834ActiveDelhiRMN7A3X1ZMN8K2L1365
policy_numberstart_datelast_paid_datetenure_(years)date_of_purchasecustomer_idsum_assured_inr/coverage_amountpremium_amountpayment_frequencyloan_eligibleloan_amount_allowedunderwriting_expensessales_agent_codepurchase_monthpurchase_quarterpurchase_yearpolicy_anniversary_dateclaim_idpolicy_type_codepolicy_codepolicy_statusstaterm_idzonal_manager_idpayment_frequency_days
9990TRS-POL-82010212-09-202312-Sep-242012-Sep-23CUST-5412531967825.0387386.80AnnuallyYes1118956.653921.03AGT-7898SeptemberQ3202312-Sep-24CLM-912030TR-UNCD988UGP-TRCD-9834SurrenderedUttarakhandRMN7A3X1ZMN8K2L1365
9991TRS-POL-52550719-08-201719-Aug-202019-Aug-17CUST-9881831446839.2160380.44MonthlyYes456004.587381.60AGT-7898AugustQ3201719-Aug-18CLM-591517TR-UNCD988UGP-TRCD-9834SurrenderedUttarakhandRMN7A3X1ZMN8K2L130
9992TRS-POL-32921220-11-202120-Nov-232020-Nov-21CUST-461264859050.2946748.59MonthlyYes319082.843629.81AGT-7898NovemberQ4202120-Nov-22CLM-862321TR-UNCD988UGP-TRCD-9834SurrenderedUttarakhandRMN7A3X1ZMN8K2L130
9993TRS-POL-14680701-05-201801-May-212001-May-18CUST-970886673382.0511430.65QuarterlyYes233625.169297.07AGT-7898MayQ2201801-May-19CLM-695622TR-UNCD988UGP-TRCD-9834SurrenderedUttarakhandRMN7A3X1ZMN8K2L190
9994TRS-POL-43204125-08-202325-Aug-242025-Aug-23CUST-563060387560.5642480.40AnnuallyYes223900.875700.98AGT-7898AugustQ3202325-Aug-24CLM-290388TR-UNCD988UGP-TRCD-9834SurrenderedUttarakhandRMN7A3X1ZMN8K2L1365
9995TRS-POL-46548707-12-201707-Dec-202007-Dec-17CUST-439248302465.1021576.84MonthlyYes154790.076890.88AGT-7898DecemberQ4201707-Dec-18CLM-934830TR-UNCD988UGP-TRCD-9834SurrenderedUttarakhandRMN7A3X1ZMN8K2L130
9996TRS-POL-95699419-04-202019-Apr-222019-Apr-20CUST-239999204448.3059548.60MonthlyNo112221.948675.45AGT-7898AprilQ2202019-Apr-21CLM-507329TR-UNCD988UGP-TRCD-9834SurrenderedUttarakhandRMN7A3X1ZMN8K2L130
9997TRS-POL-46166215-07-201915-Jul-212015-Jul-19CUST-400763658445.3967869.60AnnuallyYes322829.688633.53AGT-7898JulyQ3201915-Jul-20CLM-465856TR-UNCD988UGP-TRCD-9834SurrenderedUttarakhandRMN7A3X1ZMN8K2L1365
9998TRS-POL-40199809-03-201709-Mar-202009-Mar-17CUST-6846781748404.0362139.29QuarterlyYes826357.547101.07AGT-7898MarchQ1201709-Mar-18CLM-533930TR-UNCD988UGP-TRCD-9834SurrenderedUttarakhandRMN7A3X1ZMN8K2L190
9999TRS-POL-89455226-01-202026-Jan-222026-Jan-20CUST-510641472185.4596062.59MonthlyNo199802.213369.91AGT-7898JanuaryQ1202026-Jan-21CLM-113772TR-UNCD988UGP-TRCD-9834SurrenderedUttarakhandRMN7A3X1ZMN8K2L130